What is Machine Learning Engineer?
A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems.
Our course is focused primarly on using ML for building Artificial Intelligence systems.
Curriculum breakdown
Week 1: Python
In the first week we will reinforce the basics of Python and introduce you to 2 fundamental data management and data visualization tools: Pandas and Matplotlib.
By the end of this week you will be able to load data, explore it, clean it and visualize it in interesting ways.
Week 2: Math & Probability
This week we will introduce the advanced math and probability that you need to know to start your journey in Machine Learning. A strong understanding of math and probability is key to successfully progress in AI & ML.
Week 3: Classical ML, Tree-Based Models, K-Means Clustering and more...
You will learn some of the ML models that every Deep Learning Engineer and Data Scientist should know with a mix of both theory and practice.
Week 4: Intro to Neural Networks / TensorFlow
During this week we will introduce you to Neural Networks, Deep Learning and TensorFlow.
You will learn almighty Gradient Descent, Back Propagation, optimization methods for Deep Learning. You will gain a solid understanding of TensorFlow JS and TensorBoard.
Project: we will start working on a MNIST handwritten digit database.
Week 5: Recurrent Neural Networks & Convolutional NN
This week you will be introduced to Convolutional and Recurrent Neural Networks, and various visual (like object detection, recognition, segmentation) and text (sentiment analysis, text classification) analysis techniques.
Project: we will provide you with a few NLP (Natural Language Processing) and CV (Computer Vision) projects that you can follow through and implement by yourself.
Week 6: Generative Neural Networks (GANS and VAE)
This week we will start digging into more advanced concepts of ML, GANs (General Adversarial Neural Networks) and VAEs (Variational Autoencoders).
Project: it is of the utmost importance that you become capable of implementing your own networks. This week we will let you follow through a few selected papers and implement such networks from scratch.
Week 7: Language Models & Transformers
This week we will learn about the most important advancement of the last 4 years in Deep Learning: Transformers. We will also understand why they perform the way they do, and what are their limitations. We will work with Transfer Learning and Model Tuning to speedup model development.
Weeks 8+9: Final Project / Intro to modern AI (BERT, GPT, Diffusion Models, NeRFs, Data Annotation, deployment)
This is your time to shine! These last 2 weeks you will have 2 tasks:
- Develop your own unique project, or follow one of the pre-prepared tasks
- Teach a class about one of the advanced topics in ML
Prerequisites
For the successful completion of this course we recommend:
- Solid understanding of math
- Understanding of statistics
- Experience with Python or any other programming language
While having a background in physics, math, computer science, or telecommunications or work experience in IT may be advantageous when taking this course, it can still be valuable and informative for anyone who is interested in the subject and is prepared in some alternative ways, for example through self-learning.
Scholarships
If you fall into one of these categories your tuition would be reduced by 500€
Students over 40: because we know it’s harder to commit to learning at a certain age and we are willing to help.
Single parents: because it’s a tough job to raise kids alone, keep it up. We are here to support you!
Women in tech: we're proud to say that 45% of our graduates are women, and we're committed to achieving full gender equality. This is especially significant given that less than 9% of women work in tech, according to StackOverflow's 2022 industry survey.
How to get a scholarship:
After signing up online you will get a student's form to fill out where you can select the applicable scholarship. Your final tuition quote would be calculated based on that and you will get links for making the remaining payments.
